def _nobs_raw(self):
if self._is_rolling:
window = self._window
else:
# expanding case
window = len(self._index)
result = Series(self._time_obs_count).rolling(window, min_periods=1).sum().values
return result.astype(int)
def _nobs_raw(self):
if self._is_rolling:
window = self._window
else:
# expanding case
window = len(self._index)
result = Series(self._time_obs_count).rolling(
window, min_periods=1).sum().values
return result.astype(int)
def test_value_counts_normalized(self):
# GH12558
s = Series([1, 2, np.nan, np.nan, np.nan])
dtypes = (np.float64, np.object, 'M8[ns]')
for t in dtypes:
s_typed = s.astype(t)
result = s_typed.value_counts(normalize=True, dropna=False)
expected = Series([0.6, 0.2, 0.2],
index=Series([np.nan, 2.0, 1.0], dtype=t))
tm.assert_series_equal(result, expected)
result = s_typed.value_counts(normalize=True, dropna=True)
expected = Series([0.5, 0.5], index=Series([2.0, 1.0], dtype=t))
tm.assert_series_equal(result, expected)
def test_value_counts_normalized(self):
# GH12558
s = Series([1, 2, np.nan, np.nan, np.nan])
dtypes = (np.float64, np.object, 'M8[ns]')
for t in dtypes:
s_typed = s.astype(t)
result = s_typed.value_counts(normalize=True, dropna=False)
expected = Series([0.6, 0.2, 0.2],
index=Series([np.nan, 2.0, 1.0], dtype=t))
tm.assert_series_equal(result, expected)
result = s_typed.value_counts(normalize=True, dropna=True)
expected = Series([0.5, 0.5],
index=Series([2.0, 1.0], dtype=t))
tm.assert_series_equal(result, expected)
def _window_time_obs(self):
window_obs = Series(self._time_obs_count > 0).rolling(self._window, min_periods=1).sum().values
window_obs[np.isnan(window_obs)] = 0
return window_obs.astype(int)
